Finding Strong Groups of Friends among Friends in Social Networks
Bibliographic record
Abstract
Over the past few years, the rapid growth and the exponential use of social digital media has led to an increase in popularity of social networks and the emergence of social computing. In general, social networks are structures made of social entities (e.g., individuals) that are linked by some specific types of interdependency such as friendship. Most users of social media (e.g., Face book, Google+, Linked In, My Space, Twitter) have many linkages in terms of friends, connections, and/or followers. Among all these linkages, some of them are more important than another. For instance, some friends of a user may be casual ones who acquaintances met him at some points in time, whereas some others may be friends that care about him in such a way that they frequently post on his wall, view his updated profile, send him messages, invite him for events, and/or follow his tweets. In this paper, we apply data mining techniques to social networks to help users of the social digital media to distinguish these important friends from a large number of friends in their social networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".